LASI: Workshop on social learning analytics

I spent last week in California at Center for Educational Research at Stanford (CERAS), attending the Learning Analytics Summer Institute.  This was a strategic five-day event, July 1-5, 2013, co-organized by SoLAR and Stanford University. The twin objectives of the event were to build the field of learning analytics and to develop the skills and knowledge of participants so that they can go on to research and teach in the field.

Together with Caroline Haythornthwaite, Stephanie Teasley, Shane Dawson and Dan Suthers, I ran an afternoon workshop on Social Learning Analytics. My section focused on discourse analytics and disposition analytics.

I returned to a recurrent theme of my analytics presentations – don’t start with the data, start with the pedagogy. In this case, starting points could be:

• How do people learn socially and in social situations?
• How can we use big data to facilitate that process?

Our workshop discussion focused on how social learning analytics might be implemented in a physical space. My presentation makes mention of a research project in the building where I work, which influenced people’s behaviour in the building through the use of twinkly lights and real-time displays of behavioural data. If you are interested in finding out more about this, the researchers have published more about it.

LASI workshop social learning analytics from Rebecca Ferguson

Analytics To Identify Learning Dialogue in Online Discussions

On 11 April, I presented a full paper at the learning analytics and knowledge conference, LAK13, in Leuven, Belgium.

The paper, ‘An Evaluation of Learning Analytics To Identify Exploratory Dialogue in Online Discussions‘ was co-authored by Zhongyu Wei of the Chinese University of Hong Kong, Yulan He, now at Aston University, and Simon Buckingham Shum from The Open University.

Learning analytics to identify exploratory dialogue in online discussions from Rebecca Ferguson

Paper abstract:

Social learning analytics are concerned with the process of knowledge construction as learners build knowledge together in their social and cultural environments. One of the most important tools employed during this process is language. In this paper we take exploratory dialogue, a joint form of co-reasoning, to be an external indicator that learning is taking place. Using techniques developed within the field of computational linguistics, we build on previous work using cue phrases to identify exploratory dialogue within online discussion. Automatic detection of this type of dialogue is framed as a binary classification task that labels each contribution to an online discussion as exploratory or non-exploratory. We describe the development of a self-training framework that employs discourse features and topical features for classification by integrating both cue-phrase matching and k-nearest neighbour classification. Experiments with a corpus constructed from the archive of a two-day online conference show that our proposed framework outperforms other approaches. A classifier developed using the self-training framework is able to make useful distinctions between the learning dialogue taking place at different times within an online conference as well as between the contributions of individual participants.

Doug Clow liveblogged the presentation.

Photo from gr0uch0‘s excellent LAK13 conference set.

Discourse-Centric Learning Analytics

On 8 April I co-chaired the 1st International Workshop on Discourse-centric Learning Analytics, which took in place with the third Learning Analytics and Knowledge conference (LAK13) in Leuven, Belgium.

The workshop began with a keynote by David Williamson Shaffer on ‘How Research into Epistemics Might Inform DCLA’.

This was followed by six papers – one of which I had co-authored.

• Analysis of Discourse and the Importance of Time (Gregory Dyke, Elijah Mayfield, Iris Howley, David Adamson, Carolyn P. Rosé)
• Leveraging CSCL Research Analyzing Online Discussion to Inform DCLA (Alyssa Friend Wise)
• Discourse, Computation and Context – Sociocultural DCLA Revisited (Simon Knight and Karen Littleton)
• XIP Dashboard: Visual Analytics from Automated Rhetorical Parsing of Scientific Metadiscourse (Duygu Simsek, Simon Buckingham Shum, Ágnes Sándor, Anna De Liddo, Rebecca Ferguson)
• Virtual Communities of Practice in Academia: An Automated Discourse Analysis (Nicolae Nistor, Beate Baltes, George Smeaton, Mihai Dascalu, Dan Mihaila, & Stefan Trausan-Matu)
• OpenEssayist: Extractive Summarisation & Formative Assessment of Free-Text Essays (Nicolas Van Labeke, Denise Whitelock, Debora Field, Stephen Pulman, John Richardson)

The workshop concluded with a discussion session.

The event was liveblogged by Doug Clow.

Photograph by gr0uch0.

 

 

 

Social learning analytics

I have a new article out in the journal of Educational Technology and Society, focused on social learning analytics. These analytics use data generated by learners’ online activity in order to identify behaviours and patterns within the learning environment that signify effective process. The intention is to make these visible to learners, to learning groups and to teachers, together with recommendations with the potential to spark and support learning.

Buckingham Shum, S., & Ferguson, R. (2012). Social Learning Analytics. Educational Technology & Society, 15 (3), 3–26.

Abstract

We propose that the design and implementation of effective Social Learning Analytics (SLA) present significant challenges and opportunities for both research and enterprise, in three important respects. The first is that the learning landscape is extraordinarily turbulent at present, in no small part due to technological drivers. Online social learning is emerging as a significant phenomenon for a variety of reasons, which we review, in order to motivate the concept of social learning. The second challenge is to identify different types of SLA and their associated technologies and uses. We discuss five categories of analytic in relation to online social learning; these analytics are either inherently social or can be socialised. This sets the scene for a third challenge, that of implementing analytics that have pedagogical and ethical integrity in a context where power and control over data are now of primary importance. We consider some of the concerns that learning analytics provoke, and suggest that Social Learning Analytics may provide ways forward. We conclude by revisiting the drivers and trends, and consider future scenarios that we may see unfold as SLA tools and services mature.